Onramp — Seed → Steady State

1. The system generates candidate intent questions from the vertical seed and its own doctrine — monetization posture, risk tolerance, brand constraints, off-limits topics, takeover vs cold-start intent, success horizon.
2. Each candidate is fork-tested: does the answer change what the system would build? Questions whose answers don't fork downstream work are eliminated, not asked. ML's attention is priced; questions are kept few and large.
3. The bloom stops at saturation — when further questions no longer move the plan.

Ramp rules

• Build thresholds are loose per progressive commitment. Young artifacts and young teams are cheap to revise, so early corroboration requirements are minimal — additive knowledge (playbooks, patterns, map nodes) commits at threshold 1; worker-create at 1 with mandatory review; the personality ethos gate loosest of all pre-lock-in. The consequence dial still governs: what forks heavily (architecture blueprint, URL taxonomy, persona lock-in) stays expensive even during build.
• Graduation is per-gate and evidence-based — NEVER calendar-based. Each gate carries its own criteria: N clean merges, population stabilized (additive-discovery rate below floor, majority of proposals refinements), baselines existing, first graded outcomes live, reviewer find-rates flooring. A gate graduates when its evidence says the loose setting is no longer safe-because-cheap, not when a date passes. Because gates graduate independently, the onramp dissolves gradually into production — no cliff, no global event.
• Graduation auto-applies with a notification; the console can hold or demote.
• Two-inventory discipline: seedable artifacts (the research pyramid, cold-start playbooks, directives) are prepopulated at blitz; accumulation-only artifacts (worker-earned playbooks, rejection baselines, graded outcomes) fill only through operation, each with a declared readiness criterion — and machinery that depends on them stays declared-dormant until its inputs exist, never silently wrong.
• Numeric graduation criteria are placeholders pending calibration from real deployments. [OPEN — flag for command center]

Staged review focus

Graduation is not a ceremony and not a date. The system has graduated when, and only as long as: ↳ system/seo/ONRAMP.md:164

1. Human contact has converged to the four-category floor — intent/values changes, certified tiebreaks that survived the whole escalation ladder, key grants, final override. The measure is the escalation forward-count: how much reaches ML, in which categories, at what rate.
2. The closure rate trends to 1 — the fraction of loops the organization closes without draining work to the human. An open loop anywhere drains its missing work to ML; graduation is the observable state of that drain approaching zero while the four-category floor holds.

• All workers launch as capable as the worker ceiling permits (SYSTEM.md §7: GLM is the hard ceiling below the strategic layer; consequence above the ceiling climbs the escalation ladder, it never rents a bigger model). During build, immature directives make effective consequence-per-token maximal, so launching capable is the consequence-correct assignment, not an indulgence.
• Demotion is per-worker, on maturity criteria: directive stable for N runs, worker playbook past a depth floor, rejection rate under threshold, clean reviews. Each worker demotes to the cheaper class individually when its criteria hit; the demotion is an auditable ramp event, trialed against recorded outcomes (the cheaper class must match), and reversible on regression. Promotion back runs on metered evidence of under-power.
• The principle: model strength compensates for directive immaturity; as institutional memory accumulates on disk, intelligence migrates from the model into the artifacts.
• Workers whose consequence stays permanently high are pinned at the ceiling regardless of maturity.

The DECIDE options — keep / rewrite / merge / nuke / reclaim, per page, ledgered